PENELOPE White Paper: NOVEL AI-BASED MACHINERY CERTIFICATION METHODOLOGY

dc.contributor.author Vidal, F.
dc.contributor.author Pertusa, A.M.
dc.contributor.author Rodriguez, A.
dc.contributor.author Precker, C.
dc.contributor.author Deutz, D.B.
dc.contributor.author Bracchi, V.
dc.contributor.author Rebe, A.M.
dc.contributor.author Penalva, M.
dc.contributor.author Arkouli, Z.
dc.contributor.author Babcinschi, M.
dc.contributor.author Neto, P.
dc.date.accessioned 2025-05-27T14:30:33Z
dc.date.available 2025-05-27T14:30:33Z
dc.date.issued 2025
dc.description.abstract Over the past few decades, industrial automation has significantly reshaped manufacturing and labour through the introduction of advanced technologies. In recent years, the European manufacturing landscape has undergone a transformative shift, moving beyond efficient, automated, and data-driven production toward a more human-centric, resilient, and sustainable model—a transition known as the Industry 5.0 paradigm. Industry 5.0 defines the human worker as a pivotal element in production. This paradigm leverages technology to enhance human capabilities rather than replace them, fostering an environment where humans and machines collaborate seamlessly. This transformation is driven by the adoption of collaborative robots (cobots), exoskeletons, and AI, emphasizing cooperation over full automation. Rather than isolating workers from machinery, Industry 5.0 promotes direct human-machine interaction, where industrial equipment is designed to augment human skills, reduce physical strain, and create safer, more adaptive workspaces. While this paradigm shift offers significant benefits—enhanced worker safety, increased productivity, and improved well-being—it also introduces new challenges in industrial production. As cobots, exoskeletons, and AI-driven systems become integral to workplaces, ensuring their certification for industrial use is critical. These technologies must comply with rigorous safety standards, risk assessment protocols, and regulatory frameworks to guarantee that human workers remain protected in increasingly complex industrial environments.
dc.description.sponsorship This research was funded from the EU’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 958303. This paper does not necessarily reflect the views of the European Commission.
dc.identifier.citation Vidal, F., Pertusa, A. M., Rodríguez, A., Precker, C., Deutz, D. B., Bracchi, V., Rebe, A. M., Penalva, M., Arkouli, Z., Babcinschi, M., & Neto, P. (2025). PENELOPE White Paper: NOVEL AI-BASED MACHINERY CERTIFICATION METHODOLOGY. Zenodo. https://doi.org/10.5281/zenodo.15270158 .
dc.identifier.uri https://hdl.handle.net/10921/1741
dc.language.iso en
dc.relation info:eu-repo/grantAgreement/EC/H2020/958303
dc.rights info:eu-repo/semantics/openAccess
dc.rights.license Creative Commons Attribution 4.0 International
dc.title PENELOPE White Paper: NOVEL AI-BASED MACHINERY CERTIFICATION METHODOLOGY
dc.type Other
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